Data Science Lead

last updated April 4, 2021 1:10 UTC

RedElastic

HQ: Remote

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RedElastic is looking for a talented machine learning expert to help us craft the next-generation of social platforms. Leverage your deep knowledge of first-principles in machine learning and deep learning to implement matching algorithms in the social video space. What you will do day-to-day:

  • Design, implement, and optimize machine learning platforms in order to optimize peer-to-peer matches for real-time chat
  • Design and experiment machine learning/deep learning models so the quality of matches increases over time
  • Analyze and debug prediction models; live and breath design-oriented thinking
  • Collaborate with the development team to coordinate on all aspects of development
  • Help our clients choose the right technologies for their business needs (and avoid cargo cult programming)

Benefits

RedElastic is a small company without the bureaucracy of traditional consulting firms. We didn’t start RedElastic to grow into yet another giant consulting company, we started RedElastic to create a home for the top talent in Canada so we could all build a brand we’re proud of. We’re very selective about who we hire, and even more selective about the client work we take on. This means you’ll have a direct impact on the type of work we do as a team together. The benefits of working at RedElastic:

  • Top-shelf compensation
  • Spend 2-5 days per week in our office in downtown Toronto, complete with free coffee, beer taps, and most importantly some great conversations with other talented folks; work from home the rest of the time
  • Remote applicants will be considered on a contract-basis
  • The ability to make a difference without the traditional boundaries of an enterprise role; create unique and compelling solutions without a hint of micromanagement
  • Take your career to the next level by participating in conferences, contributing to our blog, or even working on a book with us (well help you achieve your career goals in any way we can)
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